Artificial intelligence (AI) is increasingly integrated into lung cancer care, particularly within radiology and respiratory therapy, where it supports early detection, diagnostic accuracy, and longitudinal disease management. While the technical capabilities of AI have been widely explored, less attention has been given to the ethical, legal, and professional implications arising from AI-enabled collaboration between these disciplines. This scoping review aims to map and synthesize the existing literature addressing the ethical, legal, and professional dimensions of AI-supported radiology and respiratory therapy collaboration in lung cancer diagnosis and management.                                                                                                    

A scoping review methodology was employed in accordance with the PRISMA-ScR guidelines. Electronic databases including PubMed, Scopus, Web of Science, and  IEEE Xplore were searched for English-language publications published between 2010 and 2024. Eligible sources included empirical studies, reviews, policy documents, and ethical or legal analyses addressing AI applications in radiology and/or respiratory therapy within lung cancer care.                                                 

The findings reveal three interrelated thematic domains. Ethically, major concerns include data privacy, informed consent, algorithmic bias, transparency, and the risk of over-reliance on AI systems. Legally, unresolved issues surrounding liability, regulatory classification of AI as medical devices, data protection compliance, and cross-jurisdictional governance persist. Professionally, AI integration is reshaping clinical roles, interprofessional collaboration, education, and clinical culture, highlighting the need for new competencies and collaborative governance models.

Overall, the review demonstrates that successful AI integration in lung cancer care depends not only on technological performance but also on ethical responsibility, legal clarity, and adaptive professional collaboration. These findings support the need for interdisciplinary frameworks that align AI innovation with human-centered, culturally informed healthcare practice.